Abstract [en]

1. Polyploidy is associated with a plethora of phenotypic and genetic changes yielding transformative effects on species' life-history and ecology. These biological attributes can contribute to the success of species on ecological timescales, as observed in the invasion success or rapid environmental and climatic adaptation of polyploids. However, to date there has been a distinct lack of empirical evidence linking species' local extinction risk, species distributions and community structure in fragmented landscapes with interspecific variation in ploidy. 2. We aimed to investigate the relationship between levels of habitat fragmentation and patterns in both diversity and the frequency of species with different ploidy levels. We included additional persistence-and dispersal related life-history traits, to establish the relative importance of ploidy in determining species richness and frequencies following habitat fragmentation. We therefore collected plant community presence-absence data and landscape data from grassland fragments from south-central Sweden. 3. Community-level analysis uncovered that interspecific variation in ploidy proved the strongest predictor of plant community species richness and turn-over across grassland fragments. Local extinction risk decreased as ploidy increased, with diploids most prone to local extinction. 4. In the species-level analysis, ploidy outweighed the combined explanatory power of commonly used life-history traits such as clonality, dispersal mechanism and mating system; key predictors of plant species distributions across fragmented landscapes. 5. Ploidy appears to capture parallel variation in a series of advantageous genetic and life-history mechanisms which operate on ecological timescales, emerging as the strongest predictor of local extinction risk even after accounting for variation in other crucial life-history traits. Our results therefore highlight the importance of genomic traits such as ploidy and total chromosome number as valuable factors explaining and predicting local extinction risk in fragmented landscapes.